{"id":"W2949878059","doi":"10.48550/arxiv.0908.3416","title":"Taylor Expansion and Discretization Errors in Gaussian Beam Superposition","year":2009,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Superposition principle; Beam (structure); Gaussian beam; Discretization; Taylor series; Gaussian; Mathematics; Propagation of uncertainty; Physics; M squared; Mathematical analysis; Beam diameter; Optics; Algorithm; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006069181,0.0005990826,0.0007423851,0.001071232,0.0005081178,0.001154847,0.001074692,0.001214563,0.001354767],"category_scores_gemma":[0.02113903,0.0004233531,0.000626444,0.0007187038,0.002247801,0.002261422,0.001792796,0.00172784,0.0003299117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002799,"about_ca_system_score_gemma":0.001000157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004023097,"about_ca_topic_score_gemma":0.002150084,"domain_scores_codex":[0.997925,0.0006498035,0.00009777426,0.0001537843,0.001021213,0.0001523318],"domain_scores_gemma":[0.9879095,0.008805421,0.0006482616,0.001007215,0.001453856,0.0001756848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003007583,0.00007198687,0.0031224,0.0001952716,0.00007058792,0.000252347,0.0003956515,0.659759,0.02227806,0.2692844,0.001475385,0.04279427],"study_design_scores_gemma":[0.000007064602,0.00001932942,0.000149306,0.00001689553,0.000005704946,0.00002075549,0.00001458309,0.9801654,0.003332711,0.0156966,0.0005601086,0.00001163377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03141927,0.0003633175,0.9639488,0.0003338618,0.0001034949,0.0000244411,0.00003072584,0.0002338053,0.003542427],"genre_scores_gemma":[0.6462107,0.0008090939,0.3461775,0.0002312463,0.0001370841,0.000130761,0.0001338714,0.000508139,0.005661582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006069181,"threshold_uncertainty_score":0.03209728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04845174998065895,"score_gpt":0.1939450773181646,"score_spread":0.1454933273375057,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}